Regarding object tracking and monitoring, indoor location-based service is becoming more and more important nowadays. One popular approach is to use dead-reckoning method to estimate the location of a moving object in real time. Usually, the moving direction and the moving distance are estimated by inertia measurement unit (IMU). However, the performance of moving distance estimation in the dead-reckoning based approach is far from satisfactory, which is the main reason that such indoor navigation systems are still not popular now.
Estimating the speed of a moving object in an indoor environment, which may assist the location-based service, is also an open problem and no satisfactory results appear yet. The Doppler effect has been widely applied to different speed estimation systems using sound wave, microwave, or laser light. However, low speed such as human walking speed is very difficult to be estimated using Doppler shift, especially using electromagnetic (EM) waves. This is because the maximum Doppler shift is about Δf=v/cf0, where f0 is the carrier frequency of the transmitted signal, c is the speed of light, and v is the human walking speed. Under normal human walking speed v=5.0 km/h and f0=5.8 GHz, Δf is around 26.85 Hz and it is extremely difficult to estimate this tiny amount with high accuracy. In addition, these methods need line-of-sight (LOS) condition and perform poorly in a complex indoor environment with rich multipath reflections.
Most of the existing speed estimation methods that work well for outdoor environments fail to offer satisfactory performance for indoor environments, since the direct path signal is disturbed by the multipath signal in indoor environments and the time-of-arrival (or Doppler shift) of the direct path signal cannot be estimated accurately. Then, researchers focus on the estimation of the maximum Doppler frequency which may be used to estimate the moving speed. Various methods have been proposed, such as level crossing rate methods, covariance based methods, and wavelet based methods. However, these estimators provide results that are unsatisfactory because the statistics used in these estimators have a large variance and are location-dependent in practical scenarios. For example, the accuracy of one existing speed estimation method may only differentiate whether a mobile station moves with a fast speed (above 30 km/h) or with a slow speed (below 5 km/h).
Another kind of indoor speed estimation method based on the traditional pedestrian dead reckoning algorithm is to use accelerometers to detect steps and to estimate the step length. However, pedestrians often have different stride lengths that may vary up to 40% at the same speed, and 50% with various speeds of the same person. Thus, calibration is required to obtain the average stride lengths for different individuals, which is impractical in real applications and thus has not been widely adopted.
Regarding periodic motion detection, many important human vital signs such as breathing are periodic motions. Vital signs are important indicators of a person's health and well-being as well as predictors of acute medical conditions and chronic disease states for a person. Breathing rate is one of the most important vital signs, which may be measured by the number of exhalation and inhalation a person takes per minute. In addition, the breathing pattern may be highly correlated to psychological conditions of a human being, such as stress and anxiety.
Most traditional approaches for breathing monitoring are invasive in that they need physical contact of the human bodies. For instance, in hospitals, the patients are required to wear oxygen masks, Nasal cannulas, chest straps, or wearable sensors such as thermistors and pressure sensors. Another example is Polysomnography (PSG) used in sleep medicine, which typically requires a minimum of 22 wire attachments to the patient. These dedicated devices are often costly and bulky, create discomfort to the human bodies, and are limited only to clinical settings.
Currently existing non-invasive (contact-free) breathing monitoring solutions may be categorized as below.
(1) Radar-based breathing monitoring: Doppler radars are often used in breathing monitoring. They are operated by transmitting a signal and receiving a signal with a Doppler shift due to a periodic motion of objects. The breathing rates may be extracted from the Doppler shift. As a drawback, these systems use high transmission power, rely on sophisticated and expensive hardware, and use extremely large bandwidths. A vital sign monitoring system was disclosed utilizing frequency modulated continuous radar (FMCW). It used Universal Software Radio Peripheral (USRP) as the RF front-end to transmit a frequency-sweeping signal. But the additional cost and complexity of the dedicated hardware limited a large-scale deployment of FMCW radar.
(2) Wireless-sensor based breathing monitoring: The received signal strength (RSS) measurements from 802.15.4 compliant sensors on multiple 802.15.4 channels were also used for breathing detection and breathing rate estimation. Dense deployment of wireless sensors is required in these methods as additional wireless infrastructures. In addition, the specific design of frequency-hopping mechanism is required to support multiple channel measurements.
(3) Wi-Fi-based breathing monitoring: RSS is commonly used in the Wi-Fi-based breathing monitoring due to its availability on most commercial Wi-Fi network interface controllers (NICs). Measurements were also used with Wi-Fi devices for breathing estimation. But this method is accurate only when the users hold the Wi-Fi-enabled devices in close proximity to their chests.
In addition to the drawbacks mentioned above, methods (1) and (2) require design and manufacturing of special devices such as specialized radar devices or sensor network nodes, while method (3) has very low accuracy and sensitivity.
Therefore, there is a need for methods and apparatus for vital sign detection and monitoring to solve the above-mentioned problems and to avoid the above-mentioned drawbacks.